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  ▼a  1789802083

• 020

  ▼a  9781789802085
  ▼q  (electronic bk.)

• 035

  ▼a  2153721
  ▼b  (N$T)

• 035

  ▼a  (OCoLC)1104078460

• 040

  ▼a  EBLCP
  ▼b  eng
  ▼c  EBLCP
  ▼d  N$T
  ▼d  248023

• 050

  ▼a  QA76.9.D343

• 072

  ▼a  COM
  ▼x  000000
  ▼2  bisacsh

• 082

  ▼a  006.3/12
  ▼2  23

• 100

  ▼a  Datar, Radhika.

• 245

  ▼a  Hands-On Exploratory Data Analysis with R
  ▼h  [electronic resource]:
  ▼b  Become an Expert in Exploratory Data Analysis Using R Packages.

• 260

  ▼a  Birmingham:
  ▼b  Packt Publishing, Limited,
  ▼c  2019.

• 300

  ▼a  1 online resource (254 p.).

• 500

  ▼a  Description based upon print version of record.

• 500

  ▼a  Summary

• 505

  ▼a  Cover; Title Page; Copyright and Credits; Dedication; About Packt; Contributors; Table of Contents; Preface; Section 1: Setting Up Data Analysis Environment; Chapter 1: Setting Up Our Data Analysis Environment; Technical requirements; The benefits of EDA across vertical markets; Manipulating data; Examining, cleaning, and filtering data; Visualizing data; Creating data reports; Installing the required R packages and tools; Installing R packages from the Terminal; Installing R packages from inside RStudio; Summary; Chapter 2: Importing Diverse Datasets; Technical requirements

• 505

  ▼a  Converting rectangular data into R with the readr R packagereadr read functions; read_tsv method; read_delim method; read_fwf method; read_table method; read_log method; Reading in Excel data with the readxl R package; Reading in JSON data with the jsonlite R package; Loading the jsonlite package; Getting data into R from web APIs using the httr R package; Getting data into R by scraping the web using the rvest package; Importing data into R from relational databases using the DBI R package; Summary; Chapter 3: Examining, Cleaning, and Filtering; Technical requirements; About the dataset

• 505

  ▼a  Reshaping and tidying up erroneous dataThe gather() function; The unite() function; The separate() function; The spread() function; Manipulating and mutating data; The mutate() function; The group_by() function; The summarize() function; The arrange() function; The glimpse() function; Selecting and filtering data; The select() function; The filter() function; Cleaning and manipulating time series data; Summary; Chapter 4: Visualizing Data Graphically with ggplot2; Technical requirements; Advanced graphics grammar of ggplot2; Data; Layers; Scales; The coordinate system; Faceting; Theme

• 505

  ▼a  Installing ggplot2Scatter plots; Histogram plots; Density plots; Probability plots; dnorm(); pnorm(); rnorm(); Box plots; Residual plots; Summary; Chapter 5: Creating Aesthetically Pleasing Reports with knitr and R Markdown; Technical requirements; Installing R Markdown; Working with R Markdown; Reproducible data analysis reports with knitr; Exporting and customizing reports; Summary; Section 2: Univariate, Time Series, and Multivariate Data; Chapter 6: Univariate and Control Datasets; Technical requirements; Reading the dataset; Cleaning and tidying up the data

• 505

  ▼a  Understanding the structure of the dataHypothesis tests; Statistical hypothesis in R; The t-test in R; Directional hypothesis in R; Correlation in R; Tietjen-Moore test; Parsimonious models; Probability plots; The Shapiro-Wilk test; Summary; Chapter 7: Time Series Datasets; Technical requirements; Introducing and reading the dataset; Cleaning the dataset; Mapping and understanding structure; Hypothesis test; t-test in R; Directional hypothesis in R; Grubbs' test and checking outliers; Parsimonious models; Bartlett's test; Data visualization; Autocorrelation plots; Spectrum plots; Phase plots

• 520

  ▼a  Hands-On Exploratory Data Analysis with R puts the complete process of exploratory data analysis into a practical demonstration in one nutshell. You will understand the concepts of data analysis right from data ingestion, data cleaning, data manipulation to applying statistical techniques and visualizing hidden patterns.

• 590

  ▼a  Master record variable field(s) change: 050, 072, 082, 650

• 650

  ▼a  Data mining
  ▼x  Computer programs.

• 650

  ▼a  R (Computer program language)

• 650

  ▼a  COMPUTERS / General.
  ▼2  bisacsh

• 655

  ▼a  Electronic books.

• 700

  ▼a  Garg, Harish.

• 776

  ▼i  Print version:
  ▼a  Datar, Radhika
  ▼t  Hands-On Exploratory Data Analysis with R : Become an Expert in Exploratory Data Analysis Using R Packages.
  ▼d  Birmingham : Packt Publishing, Limited,c2019,
  ▼z  9781789804379

• 856

  ▼3  EBSCOhost
  ▼u  http://search.ebscohost.com/login.aspx?direct=true&scope=site&db=nlebk&db=nlabk&AN=2153721

• 938

  ▼a  EBL - Ebook Library
  ▼b  EBLB
  ▼n  EBL5784233

• 938

  ▼a  EBSCOhost
  ▼b  EBSC
  ▼n  2153721

• 990

  ▼a  강리원

• 991

  ▼a  eBook

• 994

  ▼a  92
  ▼b  N$T